Effect of flight plans predictability and accuracy on traffic demand forecast

Fernández, Esther Calvo; Ferrera, José Manuel Cordero

doi:10.1145/2494493.2494500

Cited by 4 publications

(7 citation statements)

References 5 publications

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“…Flights arriving at the FIR tend to be later than indicated in the initial flight plan. Flights arriving at Barcelona FIR are less delayed than flights arriving at the bigger Spanish Peninsular FIR [9]. …”

Section: Sector Traffic Typementioning

confidence: 95%

“…[11] does not rank uncertainty sources but investigates prediction performance by weather type, flight plan submission, regulation and flight type. Reference Sample time Sample airspace Analyzed prediction performance metrics [12] 286 days 754 U.S. sectors peak count [9] 30 days Spanish FIR sector entry time [10] 5 days 2 U.S. ACC's sector occupancy count, sector entry time [11] 4 days 2 U.S. ACC's sector entry time, hit rate Table 2.1 shows sample time, sample airspace, and studied prediction performance metrics used in the articles that are discussed in this section. Take note that prediction error for the reviewed papers is calculated by the "actual time" minus "predicted time".…”

Section: Individual Flight Examplementioning

confidence: 99%

“…Half of all filed commercial flight plans, for the 2 examined ACC's in U.S. are filed 90 minutes before departure [11]. Half of all filed flight plans are filed 9 hours before entry into the Spanish FIR [9]. When the flight plan filling occurs the routes for the flights are known more accurately, however the flying time to sectors is no more accurate [11].…”

Section: Flight Plan Filing Timementioning

confidence: 99%

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Performance Impact of Improved Departure Time Prediction Relative to Sector Demand & Arrival Time Predictability

Curran

Konnemann

2017

17th AIAA Aviation Technology, Integration, and Operations Conference

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This thesis deals with traffic forecasts of Airspace Users for Air Navigation Service Providers. Currently there is a high amount of uncertainty in the traffic forecast. Air Traffic Flow Managers anticipate for unforeseen traffic by increasing the forecasted maximum capacity threshold by more than 10%. The European ATM research program SESAR aims ultimately to reduce the difference between the forecast and real maximum capacity threshold by less than 3%. Prediction uncertainty results in sector over-load and sub-optimal traffic flow in the air transportation system. The objective of this thesis is to investigate sector demand predictability by quantification of the difference between real and forecast traffic, and evaluate the predictability improvement by improvement of departure time predictions.Statistical analysis is performed by plotting time & count uncertainty against look-ahead time to sector entry. On a busy day, for a look-ahead time of 2 hours and longer, flights have a higher probability to be delayed than to be earlier as planned. A comparison is made between the forecast and real number of flights entering a sector for a given time of day window. Looking at the forecast time period, it can be seen that some forecasted flights did not enter in the window anymore (out), and some additional flights entered in the window that were initially not forecasted (in). 'In' and 'out' flights can be explained due to flights being earlier or delayed, or flights deviating from the planned route. Looking at 10 minutes before entry, about 30% to 40% are in/out flights, which is a large amount. In general, for a look-ahead of 0 to 3 hours, there are more 'out' than 'in' flights resulting in an over-prediction. Over-prediction means that there are more flights anticipated than really entered for a given time window.In order to reduce over-prediction, it is suggested, taking safety into account, to reduce the number of 'out' flights that deviate from the planned route. For a high capacity Maastricht Upper Area Control sector on a normal day, a 5% decrease of these 'out' flights, reduces the over-prediction by 10%. Furthermore, flight phases that are major causes of uncertainty are descent, taxi and the slot allocation process.A departure time prediction improvement of 50% results in 20% arrival time error reduction, and 30% mean sector entry time error reduction, for a 6 hour look-ahead time. The used sensitivity method does not yield realistic sector occupancy count because the effect of changed ATC procedures due to improved predictability is not incorporated.v

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Section: Sector Traffic Typementioning

confidence: 95%

Section: Individual Flight Examplementioning

confidence: 99%

Section: Flight Plan Filing Timementioning

confidence: 99%

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Performance Impact of Improved Departure Time Prediction Relative to Sector Demand & Arrival Time Predictability

Curran

Konnemann

2017

17th AIAA Aviation Technology, Integration, and Operations Conference

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show abstract

“…Reference Sample time Sample airspace Analyzed prediction performance metrics [12] 286 days 754 U.S. sectors peak count [9] 30 days Spanish FIR sector entry time [10] 5 days 2 U.S. ACC's sector occupancy count, sector entry time [11] 4 days 2 U.S. ACC's sector entry time, hit rate Table 2.1 shows sample time, sample airspace, and studied prediction performance metrics used in the articles that are discussed in this section. Take note that prediction error for the reviewed papers is calculated by the "actual time" minus "predicted time".…”

Section: Factors Affecting Sector Demand Predictabilitymentioning

confidence: 99%

“…Take note that prediction error for the reviewed papers is calculated by the "actual time" minus "predicted time". and ATC actions style and preferences [10] of controllers Horizontal route prediction accuracy Vertical route prediction accuracy Flight speed prediction accuracy Changing airspace adaption data Weather and wind forecast accuracy Accuracy of surveillance data Flight technical and operational errors Accuracy of trajectory models Factors affecting Airspace Sector altitude class, sector demand primary traffic type predictions (departures, arrivals, [12] en route, mixed), ACC [11] Flight type Commercial, GA or military flights Factor affecting FIR Traffic type Departing or arriving demand prediction [9] 2.…”

Section: Factors Affecting Sector Demand Predictabilitymentioning

confidence: 99%

Performance Impact of Improved Departure Time Prediction Relative to Sector Demand & Arrival Time Predictability

Könnemann

2015

15th AIAA Aviation Technology, Integration, and Operations Conference

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A Design Method of the General Aviation Flight Plan Management Scheme Based on Quantitative Verification

Lingmin

Liu

et al. 2015

Human Centered Computing

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Effect of flight plans predictability and accuracy on traffic demand forecast

Cited by 4 publications

References 5 publications

Performance Impact of Improved Departure Time Prediction Relative to Sector Demand & Arrival Time Predictability

Performance Impact of Improved Departure Time Prediction Relative to Sector Demand & Arrival Time Predictability

Performance Impact of Improved Departure Time Prediction Relative to Sector Demand & Arrival Time Predictability

A Design Method of the General Aviation Flight Plan Management Scheme Based on Quantitative Verification

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